The Development of an Internet-Based Knowledge Exchange Platform for Pediatric Critical Care Clinicians Worldwide*
Bibliographic record
Abstract
OBJECTIVES: Advances in Internet technology now enable unprecedented global collaboration and collective knowledge exchange. Up to this time, there have been limited efforts to use these technologies to actively promote knowledge exchange across the global pediatric critical care community. To develop an open-access, peer-reviewed, not-for-profit Internet-based learning application, OPENPediatrics, a collaborative effort with the World Federation of Pediatric Intensive and Critical Care Societies, was designed to promote postgraduate educational knowledge exchange for physicians, nurses, and others caring for critically ill children worldwide. DESIGN: Description of program development. SETTING: International multicenter tertiary pediatric critical care units across six continents. SUBJECTS: Multidisciplinary pediatric critical care providers. INTERVENTIONS: A software application, providing information on demand, curricular pathways, and videoconferencing, downloaded to a local computer. MEASUREMENTS AND MAIN RESULTS: In 2010, a survey assessing postgraduate educational needs was distributed through World Federation of Pediatric Intensive and Critical Care Societies to constituent societies. Four hundred and twenty-nine critical care providers from 49 countries responded to the single e-mail survey request. Respondents included 68% physicians and 28% nurses who care for critically ill children. Fifty-two percent of respondents reported accessing the Internet at least weekly to obtain professional educational information. The five highest requests were for educational content on respiratory care [mechanical ventilation] (48% [38%]), sepsis (28%), neurology (25%), cardiology (14%), extracorporeal membrane oxygenation (10%), and ethics (8%). Based on these findings, and in collaboration with researchers in adult learning and online courseware, an application was developed and is currently being used by 770 registered users in 60 countries. CONCLUSIONS: We describe here the development and implementation of an Internet-based application which is among the first efforts designed to promote global knowledge exchange for physicians and nurses caring for critically ill children. This application has the potential to evolve new methods in postgraduate education. Ongoing assessment of the efficacy of Internet-based learning platforms will be necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".